3 papers
cs.LG2025
Learning to Optimize Multi-Objective Alignment Through Dynamic Reward Weighting
Yining Lu, Zilong Wang, Shiyang Li +6
Prior work in multi-objective reinforcement learning typically uses linear reward scalarization with fixed weights, which provably fails to capture non-convex Pareto fronts and thu…
cs.LG2025
Think-RM: Enabling Long-Horizon Reasoning in Generative Reward Models
Ilgee Hong, Changlong Yu, Liang Qiu +8
Reinforcement learning from human feedback (RLHF) has become a powerful post-training paradigm for aligning large language models with human preferences. A core challenge in RLHF i…
cs.CL2025
Discriminative Finetuning of Generative Large Language Models without Reward Models and Human Preference Data
Siqi Guo, Ilgee Hong, Vicente Balmaseda +6
Supervised fine-tuning (SFT) has become a crucial step for aligning pretrained large language models (LLMs) using supervised datasets of input-output pairs. However, despite being…